WORKFORCE ANALYTICS: PRIORITISING TO MAXIMISE BUSINESS IMPACT
Quick Win
Using data analytics techniques to choose a new office location. One company combined data relating to the availability and cost of talent, and supply and usage of office space to decide in which cities to locate new offices. This allowed the organisation to successfully expand operations in cities where competition for critical talent was lower.
Low
Impact
High
In any organisation, there are potentially thousands of different issues that can be explored with analytics. Narrowing down the options to identify those projects that are likely to have the greatest impact on business performance is the first, and most important, task of any HR analytics team. 65% of respondents to a recent CRF survey reported that their organisation has no process for identifying and prioritising HR analytics projects. Yet having a clearly defined process: • Allows organisations to link potential projects to key strategic priorities, which will in turn determine which projects receive support; • Helps organisations to review and reprioritise projects regularly as business demands change; • Prevents successful analytics teams from being inundated with requests that they lack the capacity to deliver. You can learn more about developing a process for identifying business-critical analytics projects in CRF’s report Strategic Workforce Analytics. But how do you prioritise those projects, once identified? Nigel Guenole and colleagues suggest using a Complexity-Impact Matrix, in which the anticipated impact of proposed projects is measured against their complexity to guide prioritisation. Impact refers to the fact that analytics projects should deliver sufficient return on investment, and returns should be delivered in the short- to medium-term. Assessing the complexity of an analytics project involves taking into account factors such as ease of implementation, available skills, data, technology, and organisational politics.
Trivial Endeavour
Some organisations have spent considerable sums building sophisticated HR dashboards that slice and dice employee data and present them in highly visual ways, without taking time to understand what information business and HR leaders need to make better decisions, or the degree to which there is demand for such information in the business. Their efforts may have been better spent focusing on producing a smaller set of key metrics that can be used to drive performance.
Low
Big Bet
Combining business and employee data to understand the drivers of high performance, for example to improve the effectiveness of the sales force, improve the performance of retail stores, or improve manufacturing productivity. These types of projects can be highly complex and often require HR to work with other functions, but the payoff can be substantial.
Pet Project
Many organisations begin their workforce analytics journey by building models to analyse the causes of staff turnover or to predict who is likely to leave, because the data are readily available and it can be seen as a quick win. However, unless high turnover is a significant business issue, and reducing it would benefit the bottom line, this may not be the best use of analytics resources.
Complexity
High
When prioritising analytics projects, you may also find it helpful to keep in mind the following: • Being clear about the purpose and vision of the team, and clearly communicating this, can help to manage expectations. The vision should focus on supporting business outcomes such as growth, efficiency, productivity and profitability. • There needs to be a balance between proactively driving projects out of the business strategy and people plan and responding to ad hoc requests from the business. This will allow the team to build credibility that will in turn allow them to pursue major projects that may ultimately fail. • Prioritisation tends to be more straightforward when the head of analytics reports to, and has a close relationship with, a business-oriented CHRO, who can also back the team up when it has to turn down a request for help from someone influential. • More advanced analytics teams within your business are likely to be able to provide advice and direction on the best way to prioritise. • Although prioritisation means having to say no at times, sometimes people are sufficiently senior or influential that it is impossible to turn down their requests.